Machine Learning In Python For Dynamic Process Systems
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Machine Learning In Python For Dynamic Process Systems
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Author : Ankur Kumar
language : en
Publisher: MLforPSE
Release Date : 2023-06-01
Machine Learning In Python For Dynamic Process Systems written by Ankur Kumar and has been published by MLforPSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-01 with Computers categories.
This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning for dynamic process modeling. Upon completion, readers will be able to confidently navigate the system identification literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into three parts. Part 1 of the book provides perspectives on the importance of ML for dynamic process modeling and lays down the basic foundations of ML-DPM (machine learning for dynamic process modeling). Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the different modeling requirements and process characteristics that determine a model’s suitability for a problem at hand. These include, amongst others, presence of multiple correlated outputs, process nonlinearity, need for low model bias, need to model disturbance signal accurately, etc. Part 3 is focused on artificial neural networks and deep learning. The following topics are broadly covered: · Exploratory analysis of dynamic dataset · Best practices for dynamic modeling · Linear and discrete-time classical parametric and non-parametric models · State-space models for MIMO systems · Nonlinear system identification and closed-loop identification · Neural networks-based dynamic process modeling
Machine Learning In Python For Process Systems Engineering
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Author : Ankur Kumar
language : en
Publisher: MLforPSE
Release Date : 2022-02-25
Machine Learning In Python For Process Systems Engineering written by Ankur Kumar and has been published by MLforPSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-25 with Computers categories.
This book provides an application-focused exposition of modern ML tools that have proven useful in process industry and hands-on illustrations on how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, inferential modeling, dimensionality reduction, and process control. This book considers unique characteristics of industrial process data and uses real data from industrial systems for illustrations. With the focus on practical implementation and minimal programming or ML prerequisites, the book covers the gap in available ML resources for industrial practitioners. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning. The readers will find all the resources they need to deal with high-dimensional, correlated, noisy, corrupted, multimode, and nonlinear process data. The book has been divided into four parts. Part 1 provides a perspective on the importance of ML in process systems engineering and lays down the basic foundations of ML. Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the various characteristics of industrial process systems. Part 3 is focused on artificial neural networks and deep learning. Part 4 covers the important topic of deploying ML solutions over web and shows how to build a production-ready process monitoring web application. Broadly, the book covers the following: Varied applications of ML in process industry Fundamentals of machine learning workflow Practical methodologies for pre-processing industrial data Classical ML methods and their application for process monitoring, fault diagnosis, and soft sensing Deep learning and its application for predictive maintenance Reinforcement learning and its application for process control Deployment of ML solution over web
Machine Learning In Python For Process And Equipment Condition Monitoring And Predictive Maintenance
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Author : Ankur Kumar
language : en
Publisher: MLforPSE
Release Date : 2024-01-12
Machine Learning In Python For Process And Equipment Condition Monitoring And Predictive Maintenance written by Ankur Kumar and has been published by MLforPSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-12 with Computers categories.
This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring , and predictive maintenance solutions in process industry . The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into seven parts. Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Different types of control charts and time-series pattern matching methodologies are discussed. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications. Broadly, the book covers the following: Exploratory analysis of process data Best practices for process monitoring and predictive maintenance solutions Univariate monitoring via control charts and time series data mining Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.) Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes Fault detection and diagnosis of rotating machinery using vibration data Remaining useful life predictions for predictive maintenance
Machine Learning In Python For Visual And Acoustic Data Based Process Monitoring
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Author : Ankur Kumar
language : en
Publisher: MLforPSE
Release Date : 2024-04-24
Machine Learning In Python For Visual And Acoustic Data Based Process Monitoring written by Ankur Kumar and has been published by MLforPSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-24 with Computers categories.
This book is designed to help readers gain quick familiarity with deep learning-based computer vision and abnormal equipment sound detection techniques. The book helps you take your first step towards learning how to use convolutional neural networks (the ANN architecture that is behind the modern revolution in computer vision) and build image sensor-based manufacturing defect detection solutions. A quick introduction is also provided to how modern predictive maintenance solutions can be built for process critical equipment by analyzing the sound generated by the equipment. Overall, this short eBook sets the foundation with which budding process data scientists can confidently navigate the world of modern computer vision and acoustic monitoring.
13th International Symposium On Process Systems Engineering Pse 2018 July 1 5 2018
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Author : Mario R. Eden
language : en
Publisher: Elsevier
Release Date : 2018-07-19
13th International Symposium On Process Systems Engineering Pse 2018 July 1 5 2018 written by Mario R. Eden and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-19 with Technology & Engineering categories.
Process Systems Engineering brings together the international community of researchers and engineers interested in computing-based methods in process engineering. This conference highlights the contributions of the PSE community towards the sustainability of modern society and is based on the 13th International Symposium on Process Systems Engineering PSE 2018 event held San Diego, CA, July 1-5 2018. The book contains contributions from academia and industry, establishing the core products of PSE, defining the new and changing scope of our results, and future challenges. Plenary and keynote lectures discuss real-world challenges (globalization, energy, environment and health) and contribute to discussions on the widening scope of PSE versus the consolidation of the core topics of PSE. - Highlights how the Process Systems Engineering community contributes to the sustainability of modern society - Establishes the core products of Process Systems Engineering - Defines the future challenges of Process Systems Engineering
Scale Space And Variational Methods In Computer Vision
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Author : Luca Calatroni
language : en
Publisher: Springer Nature
Release Date : 2023-05-09
Scale Space And Variational Methods In Computer Vision written by Luca Calatroni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-09 with Computers categories.
This book constitutes the proceedings of the 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023, which took place in Santa Margherita di Pula, Italy, in May 2023. The 57 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Inverse Problems in Imaging; Machine and Deep Learning in Imaging; Optimization for Imaging: Theory and Methods; Scale Space, PDEs, Flow, Motion and Registration.
14th International Symposium On Process Systems Engineering
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Author : Yoshiyuki Yamashita
language : en
Publisher: Elsevier
Release Date : 2022-06-24
14th International Symposium On Process Systems Engineering written by Yoshiyuki Yamashita and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-24 with Technology & Engineering categories.
14th International Symposium on Process Systems Engineering, Volume 49 brings together the international community of researchers and engineers interested in computing-based methods in process engineering. The conference highlights the contributions of the PSE community towards the sustainability of modern society and is based on the 2021 event held in Tokyo, Japan, July 1-23, 2021. It contains contributions from academia and industry, establishing the core products of PSE, defining the new and changing scope of our results, and covering future challenges. Plenary and keynote lectures discuss real-world challenges (globalization, energy, environment and health) and contribute to discussions on the widening scope of PSE versus the consolidation of the core topics of PSE. - Highlights how the Process Systems Engineering community contributes to the sustainability of modern society - Establishes the core products of Process Systems Engineering - Defines the future challenges of Process Systems Engineering
Artificial Neural Networks And Machine Learning Icann 2023
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Author : Lazaros Iliadis
language : en
Publisher: Springer Nature
Release Date : 2023-09-21
Artificial Neural Networks And Machine Learning Icann 2023 written by Lazaros Iliadis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-21 with Computers categories.
The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023. The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.
Recent Innovations In Sciences And Humanities
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Author : M. Priya
language : en
Publisher: CRC Press
Release Date : 2025-03-14
Recent Innovations In Sciences And Humanities written by M. Priya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-14 with Technology & Engineering categories.
The Conference covered a wide range of themes in various disciplines. In the field of English, the conference focused on digital tools in teaching and learning, the use of AI in language teaching and learning, literature in English language teaching, teacher training, and professional development, as well as linguistic competence in English language teachers. For those interested in mathematics, the conference explored topics such as computational methods for linear and non-linear optimization, mathematical models for computer science, numerical analysis, boundary value problems, real and complex analysis, probability and statistics, fluid dynamics, sequence spaces, mathematics education, applied mathematics, differential equations, and game theory. In the field of physics, the conference delved into materials science and engineering, functional materials, computational materials science, nanomaterials and nanotechnology, structural materials, photonic materials engineering, biomaterials, biomechanics, and biosensors. Lastly, in the field of chemistry, the conference discussed materials chemistry, composite, coating, and ceramic materials, soft matter and nanoscale materials, energy systems, and materials, functional thin-film materials, nanostructures and nanofilms, polymers and biopolymers, as well as surface science and engineering.
Nature Inspired Algorithms For Big Data Frameworks
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Author : Banati, Hema
language : en
Publisher: IGI Global
Release Date : 2018-09-28
Nature Inspired Algorithms For Big Data Frameworks written by Banati, Hema and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-28 with Computers categories.
As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.